The Multiply-Accumulate (MAC) processing can be implemented using an approximative computing technique. Unlike earlier research, where the approximate range was constrained by an error accumulation issue. In contrast, a number of approximation multipliers are interleaved in this study to account for mistakes that occur when accumulate operations are performed in the opposite direction. Create the roughly 4-2 compressors that produce mistakes in the opposite direction while first reducing the computational expenses for the balanced error accumulation. Then, to provide a comparable error distance, positive and negative multipliers are painstakingly developed based on the probabilistic analysis. According to simulation results on various real-world applications, the suggested MAC processing offers the energy-efficient computing scenario by widening the range of approximatively portions. The core-level energy is decreased by the proposed interleaving strategy.

Convolutional Neural Networks, Multiplier, Image Processing, Approximate Computing,Multiplier.